TRIPLE HELIX noms.pmd

W-08Towards a Quantitative Model for Maturity Assessment in Technology ParksMarcelo Amaral, International Institute of Triple Helix, BrazilLygia Magacho, Instituto Genesis - Pontifìcie Universidade Católica do Rio de Janeiro, BrazilMarcos Lima, École de Management Léonard de Vinci, FranceThe expression “maturity model” can be understood as a set of criteria used to analyze a social phenomenon and to define itslevels of development. Maturity models give organizations a powerful tool for assessing their degree of development and facilitateplanning the necessary steps to evolve toward a more mature level. They are based on the premise that people, processes,functional areas and organizations as a whole evolve through learning and may achieve more mature stages of development.They have been proposed over time and used to describe a wide variety of phenomena, both in overall organizational developmentas well as concerning particular management functions. A widespread example is the Capability Maturity Model Integration(CMMI) framework, established by the Software Engineering Institute in the 90s (Kezner, 2005). Maturity models differ in thenumber of levels, types of criteria and variables, as well as focus areas (Burn 1994, King & Teo, 1997 apud Rocha & Vasconcelos,2004).Da Poian (2008) is among the many authors (Luger & Goldstein, 1991; Cabral & Dahab, 1998; Longhi, 1999; Zouain, 2003;Nilsson, 2006) that identify critical success factors in deploying technology parks. In his approach, eleven essential technologypark success factors are proposed: 1) time of deployment; 2) government support; 3) participation of the local community; 4)involvement of Universities and Research Centers; 5) support from financial institutions; 6) the presence of corporate andinstitutional anchor tenants; 7) appropriate urbanization and facilities, including transport and communication infrastructure; 8)competent, dynamic and creative management structure; 9) quality of leadership; 10) outreach / promotion / animation developmentand 11) quality of life and working environment.This paper proposes the transformation Da Poian’s eleven qualitative criteria in a quantitative evaluation scale, enabling thedevelopment of a metric for comparing Technology Parks and allowing their classification into different levels of maturity. To thisend, each of the eleven criteria can be assessed in a range from 1 to 4, where 1 means "bad or unimportant criterion", 2 means“moderate or average importance", 3 means that the park “satisfactorily meets the criterion” and 4 means that the project is"very good or excellent" in the considered aspect.After performing a few simulations, we concluded that it would be necessary to attribute weights to the eleven criteria, sincethey have different importance according to their environments. We chose to attribute 1 for minor, 2 for medium and 3 for majorrelevance. We determined that the sum of the weights would be 25 so that the multiplication of the evaluation scales times theweighting scale allows for a minimum of 25 points and a maximum of 100 points. Parks located in the range between 80 and 100points can be considered to have a high degree of maturity; between 60 and 80 points would indicate an intermediate level ofevolution; finally, parks below 60 points are thought to have a low level of maturity.The pilot application of this model was carried out by comparing the Petropolis Technopolis project, in Rio de Janeiro, with itsinspiration, the French Sophia Antipolis complex. The former scored 53 in our overall evaluation, which positions it in the upperlimits of the lower levels of development, whereas Sophia Antipolis scored 86, thus positioned in the lower end of the welldeveloped projects. Petropolis ranked highest in quality of life (9 points), with lower-average rankings (6 points) in local communityinvolvement, financial support, presence of anchor tenants, facilities and outreach. All other criteria were ranked very low.Sophia was ranked excellent (12) in quality of life, facilities and government support, higher-average (8-9) in time of deployment,anchor tenants, leadership and outreach and lower-average or low in the remaining criteria.In the concluding remarks, we found that the proposed maturity model approach needs yet to be developed further as there aresignificant discussions in the social sciences concerning the adaptation of qualitative assessments into quantitative scales.There must be a greater consensus on the key criteria for evaluating such projects, as well as the ideal weight to assign to eachcriterion in specific contexts. Moreover, in order to reduce the degree of subjectivity in the allocation of "notes" to each item byfew analysts, such as was the case in this simulation, it is suggested that this tool is referred to the largest possible number ofactors involved with the projects under assessment. Thus, one can statistically control the bias of each individual participantwith measures of dispersion. Finally, it should be noted that the quantitative analysis of the proposal should not be used as asubstitute but as a complement to the qualitative approach. It is suggested that the weaknesses identified are qualitativelydiscussed in forums of local development in order to facilitate the production of consensus among all stakeholders in theacademic, business and government spheres of influence.Madrid, October 20, 21 & 22 - 201015